research-article

A Method of Recognition of Arabic Cursive Handwriting

Authors:

H. Almuallim,

S. YamaguchiAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 9, Issue 5

Pages 715 - 722

https://doi.org/10.1109/TPAMI.1987.4767970

Published: 01 May 1987 Publication History

Abstract

In spite of the progress of machine recognition techniques of Latin, Kana, and Chinese characters over the two past decades, the machine recognition of Arabic characters has remained almost untouched. In this correspondence, a structural recognition method of Arabic cursively handwritten words is proposed. In this method, words are first segmented into strokes. Those strokes are then classified using their geometrical and topological properties. Finally, the relative position of the classified strokes are examined, and the strokes are combined in several steps into a string of characters that represents the recognized word. Experimental results on texts handwritten by two persons showed high recognition accuracy.

Cited By

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Cui XLi YXu L(2024)Deep Neural Network with a Characteristic Analysis for Seal Stroke RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/367688323:11(1-22)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3676883
Ali ASuresha M(2020)Survey on Segmentation and Recognition of Handwritten Arabic ScriptSN Computer Science10.1007/s42979-020-00187-y1:4Online publication date: 6-Jun-2020
https://dl.acm.org/doi/10.1007/s42979-020-00187-y
Kumar MJindal MSharma RJindal S(2019)Character and numeral recognition for non-Indic and Indic scripts: a surveyArtificial Intelligence Review10.1007/s10462-017-9607-x52:4(2235-2261)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10462-017-9607-x
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Index Terms

A Method of Recognition of Arabic Cursive Handwriting
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 9, Issue 5

May 1987

133 pages

ISSN:0162-8828

Editor:
Steven L. Tanimoto

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Publisher

IEEE Computer Society

United States

Publication History

Published: 01 May 1987

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Cui XLi YXu L(2024)Deep Neural Network with a Characteristic Analysis for Seal Stroke RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/367688323:11(1-22)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3676883
Ali ASuresha M(2020)Survey on Segmentation and Recognition of Handwritten Arabic ScriptSN Computer Science10.1007/s42979-020-00187-y1:4Online publication date: 6-Jun-2020
https://dl.acm.org/doi/10.1007/s42979-020-00187-y
Kumar MJindal MSharma RJindal S(2019)Character and numeral recognition for non-Indic and Indic scripts: a surveyArtificial Intelligence Review10.1007/s10462-017-9607-x52:4(2235-2261)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10462-017-9607-x
Lamsaf AAitkerroum MBoulaknadel SFakhri Y(2018)Lines segmentation and word extraction of Arabic handwritten textProceedings of the 3rd International Conference on Smart City Applications10.1145/3286606.3286831(1-7)Online publication date: 10-Oct-2018
https://dl.acm.org/doi/10.1145/3286606.3286831
Gagaoua MGhilas HTari ACheriet MLaouar RKajan E(2017)Distribution, Directional, structural and concavity features for historical Arabic handwritten recognitionProceedings of the International Conference on Computing for Engineering and Sciences10.1145/3129186.3129200(70-75)Online publication date: 22-Jul-2017
https://dl.acm.org/doi/10.1145/3129186.3129200
Verma KSharma R(2017)Comparison of HMM- and SVM-based stroke classifiers for Gurmukhi scriptNeural Computing and Applications10.1007/s00521-016-2309-528:1(51-63)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s00521-016-2309-5
Abdelaziz IAbdou SAl-Barhamtoshy H(2016)A large vocabulary system for Arabic online handwriting recognitionPattern Analysis & Applications10.1007/s10044-015-0526-719:4(1129-1141)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s10044-015-0526-7
Mahmoud SAhmad IAl-Khatib WAlshayeb MTanvir Parvez MMärgner VFink G(2014)KHATTPattern Recognition10.1016/j.patcog.2013.08.00947:3(1096-1112)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1016/j.patcog.2013.08.009
Parvez MMahmoud S(2013)Offline arabic handwritten text recognitionACM Computing Surveys10.1145/2431211.243122245:2(1-35)Online publication date: 12-Mar-2013
https://dl.acm.org/doi/10.1145/2431211.2431222
Elzobi MAl-Hamadi AAl Aghbari ZDings L(2013)IESK-ArDBInternational Journal on Document Analysis and Recognition10.1007/s10032-012-0190-z16:3(295-308)Online publication date: 1-Sep-2013
https://dl.acm.org/doi/10.1007/s10032-012-0190-z
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Recommendations

Optical Character Recognition for Cursive Handwriting

Fuzzy stroke analysis of Devnagari handwritten characters

Arabic online handwriting recognition: a survey

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